Mathematical Challenge August 2017

September 12, 2017

Large scale optimization problems arise often due to a large number of optimization variables or, as in machine learning tasks, due to a large number of training data to fit the model on. Distributed optimization algorithms, where computations are performed in parallel relying only on local observations and information, are appealing to reduce computational time and sometimes, in case of lack of a centralized access to information for example, unavoidable.